INTERNATIONAL JOURNAL OF TECHNOLOGICAL EXPLORATION AND LEARNING (IJTEL)
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A Study on Indian License Plate Identification
Dipankar Bhattacharya
Department of Computer Science & Engineering
Calcutta Institute Of Technology
Howrah, India
II.
Abstract— Vehicle License Plate Recognition is a very
significant & complex area of Image processing which has
various applications. In situations where no standard number
plate format and font is maintained, identification becomes
difficult. In India the situation is very common where no
standard font and size is maintained while designing Car
Registration Plates. In this paper, we discussed effect of Edge
Detection and ANN is used for character recognition on
identification of Vehicle Number Plates with various types of
fonts. Experimental results show that the performance of the
proposed method is simple and satisfactory, which can provide
very high percentage of accuracy most of the time.

Keywords- Character Recognition; Edge Detection; Artificial
Neural Network.
I.
INTRODUCTION
Vehicle License Plate Recognition (LPR) has become a
significant application in the transportation system. It can be
used in many applications such as entrance admission, security,
parking control, and road traffic control, and speed control.
However, when no standard font style and size is maintained in
different Vehicle Number Plates, identification of characters in
a Number Plate becomes difficult .Detection of location of the
Number Plate from image is also difficult task. All these
problems restrict the use of automated Vehicle Number Plates
Identification System in various complex situations and places.
The basic idea behind our work is to use morphological edge
detection method and simple character recognition technique in
a complex scenario to find if the results are satisfactory. ANN
is regarded as a common methodology for identification of
characters from an image. When the characters are of
predefined font this produces very satisfactory result. However
in complex situation like Vehicle License Plate Recognition
where the images are noisy and fonts may not be of a same
type, applying this methodology and obtaining a significant
successful outcome is challenging. In our proposed technique
we have experimented on all these possibilities and displayed
the results, which are satisfactory.
This paper is organized as follows. The proposed model
and steps of the system are explained in section 2.
Experimental results and analysis are presented in section 3.
The Conclusion is summarized in section 4.

Digitization
In this process, the input image is sampled into a binary
window, which forms the input to the recognition system. In
the above figure, the alphabet A has been digitized into digital
cells, each having a single color, either black or white. It
becomes important for us to encode this information in a form
meaningful to a computer. For this, we assign a value +1 to
each black pixel and 0 to each white pixel and create the binary
image matrix, which is shown in the Fig.1. Digitization of an
image into a binary matrix of specified dimensions makes the
input image invariant of its actual dimensions. Hence an image
of whatever size gets transformed into a binary matrix of fixed
pre-determined dimensions. This establishes uniformity in the
dimensions of the input and stored patterns as they move
through the recognition system.

Figure 1. Digitazation Process

B. Morphological Edge Detection
Then we follow the following four steps.


Boundary extraction,



Hit-or-miss transform



Thinning operation



Pruning operation

1) Boundary Extraction
Our first step for morphological edge detection is boundary
extraction. At first we take a structure element and make
erosion on the image by this structure element. Then we erode
the binary image.
The erosion may be defined as:

This expression shows that we make the erosion of image
A by the structure element B. The structure element is entirely
contained within A. In fact, erosion reduces the number of
pixels from the object boundary. The number of pixels
removed depends on the size of structure element.
Let us consider
A = {(1,0),(1,1),(1,2),(0,3),(1,3),(2,3),(3,3), (1,4)}
B = {(0, 0), (1, 0)}
A Θ B = {(0, 3), (1, 3), (2, 3)}
Figure 5. Snapshot of boundary extract image

(a)

(b)
Figure 2.

(a) Original image (b) Eroted Image

After eroding we subtract this eroded image from the
binary image. Then we got a boundary extracted image, which
is two or more pixel thick noiseless binary image. The
boundary extraction may be defined as:
C = A – (A Θ B)

Figure 3.

(2)

2) HIT-OR-MISS Transform
The hit-or-miss transformation may be defined as
morphological operator, which is used for making one pixel
thick image from two or more pixel thick image. A small odd
size mask typically, 3 × 3, can be scanned over a binary image.
The hit-and-miss transformation operates as a binary matching
between the image and the structuring element. If the
foreground and background pixels in the structuring element
exactly match the foreground and background pixels in the
image, then the pixel underneath the origin of the structuring
element is set to the foreground color. If it does not, that pixel
is set to background color. The Hit-or-Miss transform may also
be expressed in terms of erosion as:
A B = (A Θ B1) ∩ (Ac Θ B2)
(3)
In this process, at first we make the Erosion Operation on
the image A with the structure element B1. Then calculate the
complement of image A. Then, again we make the Erosion
Operation on the image which is the complement of A with the
structure element B2. At last we make the Intersection
Operation between the two eroded image and we find the result
of the Hit-or-Miss Transformation.

Snapshot of original image

Figure 6. Snapshot of hit-or-miss image

Figure 4. Snapshot of binary image

IJTEL, ISSN: 2319-2135, VOL.3, NO.2, APRIL 2014

3) Thinning Operation
Thinning is a morphological operation that is used to
remove selected foreground pixels from binary images,
somewhat like erosion or. It can be used for several
applications, but is particularly useful for skeletonization. In
this mode it is commonly used to tidy up the output of edge
detectors by reducing all lines to single pixel thickness.
Thinning is normally only applied to binary images, and

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produces another binary image as output. The thinning
operation is related to the hit-or-miss transform.
Thinning Operation may be defined by the following
expression:
A Ă&#x2DC; B = A â&#x20AC;&#x201C; (A

B)

(4)

In this operation we must be subtract the Hit-or-Miss
Transformed image from the Boundary extracted image.

C. CORNER DETECTION
Corner detection is an important aspect in image processing
and researchers find many practical applications in it. There are
many algorithm for detect corner like, Corner detector based on
global and local curvature properties. But, we use the Freeman
chain codes algorithm for detect true corner of an object in an
image.
1) Chain Codes Algorithm
Chain codes are used to represent a boundary by a
connected sequence of straight-line segments of specified
length and direction. Typically, this representation is based on
4- or 8-connectivity of the segments. The direction of each
segment is coded by using a numbering scheme such as ones
0

4) Prunning Operation
Thinning operation may be reduced the thickness of an
object in an image to a one pixel wide skeleton representation.
The problem with this operation is that they leave behind extra
tail pixels.
The tail pixels required to remove from this image. The
process of removing these tail pixels is called as Pruning. In
morphological pruning operation we use the hit-and miss
operation with this image by a composite structuring element.
After the pruning operation we got the original edge of an
object in an image. The morphological operation may be
defined as:
C=A

B

(5)

1

3

4
8
7
5
6
Figure 10. 8-directional chain code

The chain code of a boundary depends on the starting point.
However, the code can be normalized with respect to the
starting point by treating it as a circular sequence of direction
numbers and redefining the starting point so that the resulting
sequence of numbers forms an integer minimum magnitude.
We can normalize for rotation by using the difference of the
chain code instead of the code itself. The difference is obtained
by counting the number of direction changes that separate two
adjacent element of the code. Another advantage of chain code
is that it is translation invariant but not as well for rotation and
scaling. Rotation can be resolved by taking difference chain
code while scaling by addressed by changing the size of the
sampling grid which the shape overlays on. Each segment of
the chain code has direction. If we want to move from one
segment to another, the angle magnitude will change.

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For example, from the above figure if we start from point
A, then the chain code for this figure is 00706444422 in 8directional. Here, From point A to point B the chain code is 0
and point B to C again 0. Here the code will not change so here
corner will not be detected. Point C to point D the chain code is
7, here the chain code is change so in point C a corner will be
detected. Point D to point E the chain code is 0, here the chain
code again change so in point D again a corner will be
detected.

so that the model can then be used to produce the output when
the desired output is unknown.
A graphical representation of an MLP is shown below.

Figure 14. Neural Network

Figure 12. Snapshot of corner detected image

D.

Separation of Character
After identifying the corner locations we extract the
Vehicle Number Plate image section from the original image.
Separation of character has been done in two phases, namely,
Horizontal Segmentation and Vertical segmentation. At first,
the image is processed row-wise to separate useful information.
After this phase we have been able to identify the series of
characters sequentially arranged from a Number plate image.
Following Vertical segmentation, which is column-wise
operation to separate information, we finally have characters
totally separated from an input Number Plate image. This
process is explained in Fig. 13.

ANN is used for character recognition. We used a MultiLayer Perceotron Neural Network (MLP NN) trained with the
back-propagation algorithm. During learning phase, characters
of the constituted database are successively presented at the
input layer of MLP network and their corresponding outputs
are compared to the desired outputs. Weights are iteratively
modified in order to obtain, at the network outputs, responses
which are as close as possible to the desired outputs.
III. EXPERIMENTAL RESULTS
From the shown Results in MATLAB environment it is
evident that the system is capable of producing computer
readable data from image containing text. In different cases
fonts of character is different from each other but when ANN
is applied on those input images, the output is quite accurate
irrespective of the fonts styles in image. The images contain
noises, which have been removed during processing as pixel
below a particular size was discarded.

Figure 13. Character Separation

E.

Character Recognition
The most common neural network model is the multilayer
perceptron (MLP). This type of neural network is known as a
supervised network because it requires a desired output in order
to learn. The goal of this type of network is to create a model
that correctly maps the input to the output using historical data
IJTEL, ISSN: 2319-2135, VOL.3, NO.2, APRIL 2014

Figure 15. Edge Detection of the Number Plate

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ACKNOWLEDGMENT
Authors of this paper sincerely thank all the members of
Computer Science & Engineering Department of Calcutta
Institute of Technology for their constant support and
encouragement. Authors also sincerely thank the Institution
Authorities for their support.
REFERENCES
[1]

V. CONCLUSION
In this paper we overviewed the problem of Vehicle
Number Plate recognition with various types of Number Plates.
We proposed a correlation based character recognition system,
which provides result with significant accuracy, which is very
simple to implement. The system has been tested on MATLAB
environment with satisfactory results. Most of the time the
input image taken from low-resolution mobile camera which
does not have very good quality image output. Given a better
device the result should increase in accuracy significantly.